all_responses.ipynb


These first few charts are not executed in linear order. I pulled them up from the bottom.

Main takeaways

  1. (q3-7) Sound familiar? Predominantly used by data scientists, researchers, and academics using Python for visualization, data wrangling, documenting research [<- needs improvement], and ML.

  2. (q19-20) Extreme pain points: autocompletion, version control, track changes.

  3. (q18c) Collaborators are either working on different parts of a project or entirely separate projects.

  4. (q18-q19) People want to publish to shared location in order to share knowledge [see RStudio Server].

  5. (q14) Most people either scale vertically or don't know how to scale.

  6. (q15) The pain points raised in scale, data, and collaboration seem addressable.

  7. (q6) Usage: Local machine/ venv > Google Colab > JupyterHub > HPC > Docker.

  8. (q3) Keep an eye on: Julia already half as popular as R and almost matching SQL. Dask as popular as Spark.

(q7) What are the most frequent use cases?

(q7) Where is Jupyter better/ worse than alternative tools?


Question-by-question insight


215 data points gathered.

Helper functions.


Survey Questions